2025-11-27
102_ Genetics to Improve Outcomes in Schizophrenia (GENios)
Research Question and Aims
International collaborative efforts in the field of schizophrenia genetics have been primarily focused on genomic discovery; case control studies have been used to successfully identify hundreds of risk factors for schizophrenia – in the form of common single-nucleotide variants (SNVs), copy number variants (CNVs) and rare coding variants (RCVs) – that are being exploited by the neuroscience community for mechanistic understanding. Nevertheless, limited progress has been made in understanding the genetic basis of outcomes in the disorder. This is in part due to the focus of genetic studies on the presence or absence of a schizophrenia diagnosis, rather than a focus on outcomes themselves. The biological basis of variation in outcomes is unknown and there are therefore no biomarkers to highlight outcome groups early in the course of illness (where interventions are likely to have the biggest impacts (PMID:28941089)) and no targets for outcome focused therapies. There is a clear and significant gap in current understanding of the genetics of outcomes in the disorder, which impedes progress towards precision psychiatry for schizophrenia (PMID: 33272362) and improved outcomes and quality of life for patients. The aim of GENios programme of research is to understand the genetic contributions to patient outcomes in schizophrenia in order to (i) illuminate the biology of poor outcomes, (ii) highlight novel drug targets, and (iii) identify genomic predictors of poor outcomes that can be leverage for patient stratification and precision medicine.
Analytic Plan
We will undertake large-scale genomic studies of outcomes in schizophrenia using existing data and within-case study designs. The choice of outcomes we have chosen to focus on has been informed through discussions with people with lived experience of psychosis and schizophrenia. We will focus on (i) treatment response (ii) psychiatric hospital admissions (iii) social and occupational functioning and (iv) relationships.
Schizophrenia will be defined as schizophrenia or schizoaffective disorder (equivalent of DSM-IV 295.xx codes).
Our approach to phenotypes is two-fold. First, we will use longitudinal data to create ‘lifetime ever’ versions of phenotypes (e.g. ever married, ever taken clozapine, etc). These definitions will be comparable to other data contributed to our study and used in multi-cohort meta-analysis. We will also use longitudinal data to create phenotypes that capture change over time. These phenotypes are either to capture characteristics of the disorder that may impact our outcomes (e.g. change in PANSS scores) or to capture trajectories in our outcomes over time. These more granular phenotypes are unlikely to be available in other cohorts. Therefore, we intend to use them to test the validity of our lifetime ever phenotypes, and understand the limitations of those broader definitions. In addition, where power allows, we will test whether genetic findings generalise to more nuanced definitions of our phenotypes. This approach will ensure that the longitudinal data available in PsyCourse will be used appropriately, with initial analyses focusing on lifetime phenotypes that maximise comparability with other cohorts, and downstream analyses only undertaken to validate our methods, or where initial findings justify them.
For our genetic analysis, first, we will conduct a comprehensive series of outcome targeted genome-wide studies of SNVs, RCVs and CNVs. This will enable us to identify specific genes that influence outcomes and TRS, and generate the fundamental resources for subsequent aims. Second, we will apply functional genomics and computational biology methods to these genomic data to identify and refine areas of biology influencing our outcomes and highlight novel drug targets. Third, we will construct genomic predictors of outcomes based on all classes of measured variation, and make these available for the development of integrated outcome prediction algorithms for precision medicine in schizophrenia. We will include individuals from multiple ancestries, identified using biogeographic inference (PMID: 30647433), performing GWAS using mixed-model designs to maximise power while minimising confounding due to heterogeneity in subpopulation size and ancestral makeup (PMID: 31217584). The availability of data covering our four outcome domains allows the use of genomic discovery approaches that go beyond the standard univariate GWAS framework, by exploiting correlation and covariance structures between phenotypes. Methodology is developing, but currently, we envisage using multiple approaches to model genomic relationships between outcomes and discover variants associated with multiple outcomes (“pleiotropic” as named in the relevant literature) e.g. MiXeR (PMID: 31160569); LAVA (PMID: 35288712, 36684006); Genomic Structural Equation Modelling (GSEM) (PMID: 31440427, 36044200); cross-phenotype meta-analytic approaches that can detect variants with pleiotropic or antagonistic effects across outcomes (PMID: 22560090, 32665545). The use of complementary approaches, each of which exploits different properties of genomic data and phenotypic covariance structures, will allow us to ascertain in detail whether known correlations in outcomes in those with schizophrenia might stem from a shared genomic basis, identify potentially pleiotropic variants for further biological investigation, and generate polygenic predictors shared across or unique to, each outcome.
Resources needed
v4_opcrit
psyc_id
gsa_id
exome_id
visit
adv
age
Antidepressants
Antipsychotics
bmi
cgi_c
cgi_s
chg_empl_stat
chg_hsng
clin_add_oth_hsp
clin_fst_ill_ep_dep
clin_fst_ill_ep_dur
clin_fst_ill_ep_hsp_dur
clin_fst_ill_ep_hsp
clin_fst_ill_ep_man
clin_fst_ill_ep_med_chg
clin_fst_ill_ep_mx
clin_fst_ill_ep_oth_end
clin_fst_ill_ep_othr
clin_fst_ill_ep_psy
clin_fst_ill_ep_slf_end
clin_fst_ill_ep_suic
clin_fst_ill_ep_symp_wrs
clin_ill_ep_snc_lst
clin_no_ep
clin_oth_hsp_dur
clin_oth_hsp_nmb
clin_othr_psy_med
clin_sec_ill_ep_dep
clin_sec_ill_ep_dur
clin_sec_ill_ep_hsp_dur
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clin_sec_ill_ep_med_chg
clin_sec_ill_ep_mx
clin_sec_ill_ep_oth_end
clin_sec_ill_ep_othr
clin_sec_ill_ep_psy
clin_sec_ill_ep_slf_end
clin_sec_ill_ep_suic
clin_sec_ill_ep_symp_wrs
cng_mar_stat
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con_problems_snc_lst
con_psy_hosp_dur
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con_psy_hosp_why_othr_txt
con_psy_hosp_why_othr
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cur_psy_trm
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disabl_pens
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evnt_prcp_it_10
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evnt_prcp_it_12
evnt_prcp_it_13
evnt_prcp_it_14
evnt_prcp_it_15
evnt_prcp_it_16
evnt_prcp_it_17
evnt_prcp_it_18
evnt_prcp_it_19
evnt_prcp_it_2
evnt_prcp_it_20
evnt_prcp_it_21
evnt_prcp_it_22
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evnt_prcp_it_24
evnt_prcp_it_25
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evnt_prcp_it_27
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evnt_prcp_it_7
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gaf
idsc_itm19
idsc_itm21
interv_date
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leq_E_41A
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leq_I_72A
leq_I_72B
leq_I_73A
leq_I_73B
lith_prd
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nrpsy_dgt_sp_bck
nrpsy_dgt_sp_frw
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nrpsy_mtv
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nrpsy_tmt_B_rt
nrpsy_vlmt_check
nrpsy_vlmt_corr
nrpsy_vlmt_lss_d
nrpsy_vlmt_lss_t
nrpsy_vlmt_rec
Other_psychiatric
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panss_g10
panss_g11
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panss_g13
panss_g14
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panss_g5
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panss_sum_gen
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panss_sum_pos
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partner
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scid_suic_ide
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sf12_itm6
sf12_itm7
sf12_itm8
sf12_itm9
smk_strt_stp
spec_emp
stp_chld
suic_attmpt_snc_lst_vst
suic_ide_snc_lst_vst
suic_note_attmpt
Tranquilizers
whoqol_dom_env
whoqol_dom_glob
whoqol_dom_phys
whoqol_dom_psy
whoqol_dom_soc
whoqol_itm1
whoqol_itm10
whoqol_itm11
whoqol_itm12
whoqol_itm13
whoqol_itm14
whoqol_itm15
whoqol_itm16
whoqol_itm17
whoqol_itm18
whoqol_itm19
whoqol_itm2
whoqol_itm20
whoqol_itm21
whoqol_itm22
whoqol_itm23
whoqol_itm24
whoqol_itm25
whoqol_itm26
whoqol_itm3
whoqol_itm4
whoqol_itm5
whoqol_itm6
whoqol_itm7
whoqol_itm8
whoqol_itm9
wrk_abs_pst_6_mths